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In this tutorial, we focus on text-to-text generation, a class of natural language generation (NLG) tasks, that takes a piece of text as input and then generates a revision that is improved according to some specific criteria (e.g., readability or linguistic styles), while largely retaining the original meaning and the length of the text.
Subjective assessment of text complexity: A dataset for german language
Babak Naderi, Salar Mohtaj, Kaspar Ensikat, and Sebastian Möller. 2019 · 1904
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Minimum bayes-risk decoding for statistical machine translation
Shankar Kumar and Bill Byrne. 2004 · 2004
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Lexico-syntactic text simplification and compression with typed dependencies
Mandya Angrosh, Tadashi Nomoto, and Advaith Siddharthan. 2014 · 2006
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Fostering digital inclusion and accessibility: The PorSimples project for simplification of Portuguese texts
Sandra Aluísio and Caroline Gasperin. 2010 · 2010
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Soylent: a word processor with a crowd inside
Michael S. Bernstein, Greg Little, Rob Miller, Björn Hartmann, Mark S. Ackerman, David R Karger, David Crowell, and Katrina Panovich. 2010 · 2010
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DSim, a Danish parallel corpus for text simplification
Sigrid Klerke and Anders Søgaard. 2012 · 2012
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Paraphrasing for style
Wei Xu, Alan Ritter, Bill Dolan, Ralph Grishman, and Colin Cherry. 2012 · 2012
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Text simplification for people with autistic spectrum disorders
C Orasan, R Evans, and I Dornescu. 2013 · 2013
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Design and annotation of the first Italian corpus for text simplification
Dominique Brunato, Felice Dell’Orletta, Giulia Venturi, and Simonetta Montemagni. 2015 · 2015
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Japanese news simplification: tak design, data set construction, and analysis of simplified text
Isao Goto, Hideki Tanaka, and Tadashi Kumano. 2015 · 2015
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Making it simplext: Implementation and evaluation of a text simplification system for spanish
Horacio Saggion, Sanja Štajner, Stefan Bott, Simon Mille, Luz Rello, and Biljana Drndarevic. 2015 · 2015
Earlier work this paper cites.
PaCCSS-IT: A parallel corpus of complex-simple sentences for automatic text simplification
Dominique Brunato, Andrea Cimino, Felice Dell’Orletta, and Giulia Venturi. 2016 · 2016
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Simpitiki: a simplification corpus for italian
Sara Tonelli, Alessio Palmero Aprosio, and Francesca Saltori. 2016 · 2016
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Optimizing statistical machine translation for text simplification
Wei Xu, Courtney Napoles, Ellie Pavlick, Quanze Chen, and Chris Callison-Burch. 2016 · 2016
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Split and rephrase
Shashi Narayan, Claire Gardent, Shay B. Cohen, and Anastasia Shimorina. 2017 · 2017
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Creative writing with a machine in the loop: Case studies on slogans and stories
Elizabeth Clark, Anne Spencer Ross, Chenhao Tan, Yangfeng Ji, and Noah A Smith. 2018 · 2018
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Hierarchical neural story generation
Angela Fan, Mike Lewis, and Yann Dauphin. 2018 · 2018
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The corpus of Basque simplified texts (CBST)
Itziar Gonzalez-Dios, María Jesús Aranzabe, and Arantza Díaz de Ilarraza. 2018 · 2018
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CLEAR – simple corpus for medical French
Natalia Grabar and Rémi Cardon. 2018 · 2018
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Adversarial example generation with syntactically controlled paraphrase networks
Mohit Iyyer, John Wieting, Kevin Gimpel, and Luke Zettlemoyer. 2018 · 2018
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Crowdsourced corpus of sentence simplification with core vocabulary
Akihiro Katsuta and Kazuhide Yamamoto. 2018 · 2018
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Simplified corpus with core vocabulary
Takumi Maruyama and Kazuhide Yamamoto. 2018 · 2018
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Dear sir or madam, may I introduce the GYAFC dataset: Corpus, benchmarks and metrics for formality style transfer
Sudha Rao and Joel Tetreault. 2018 · 2018
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Cats: A tool for customized alignment of text simplification corpora
Sanja Štajner, Marc Franco-Salvador, Paolo Rosso, and Simone Paolo Ponzetto. 2018 · 2018
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Text summarization with pretrained encoders
Yang Liu and Mirella Lapata. 2019 · 2019
Earlier work this paper cites.
ASSET: A Dataset for Tuning and Evaluation of Sentence Simplification Models with Multiple Rewriting Transformations
Fernando Alva-Manchego, Louis Martin, Antoine Bordes, Carolina Scarton, Benoît Sagot, and Lucia Specia. 2020 · 2020
Earlier work this paper cites.
French biomedical text simplification: When small and precise helps
Rémi Cardon and Natalia Grabar. 2020 · 2020
Earlier work this paper cites.
Alector: A parallel corpus of simplified French texts with alignments of misreadings by poor and dyslexic readers
Núria Gala, Anaïs Tack, Ludivine Javourey-Drevet, Thomas François, and Johannes C. Ziegler. 2020 · 2020
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Neural syntactic preordering for controlled paraphrase generation
Tanya Goyal and Greg Durrett. 2020 · 2020
Earlier work this paper cites.
Denoising diffusion probabilistic models
Jonathan Ho, Ajay Jain, and Pieter Abbeel. 2020 · 2020
Cited alongside, same era.
Neural CRF model for sentence alignment in text simplification
Chao Jiang, Mounica Maddela, Wuwei Lan, Yang Zhong, and Wei Xu. 2020 · 2020
Cited alongside, same era.
Reformulating unsupervised style transfer as paraphrase generation
Kalpesh Krishna, John Wieting, and Mohit Iyyer. 2020 · 2020
Cited alongside, same era.
Zero-shot crosslingual sentence simplification
Jonathan Mallinson, Rico Sennrich, and Mirella Lapata. 2020a · 2020
Cited alongside, same era.
FELIX: Flexible text editing through tagging and insertion
Jonathan Mallinson, Aliaksei Severyn, Eric Malmi, and Guillermo Garrido. 2020b · 2020
Cited alongside, same era.
Controllable sentence simplification
Louis Martin, Éric Villemonte De La Clergerie, Benoît Sagot, and Antoine Bordes. 2020 · 2020
Cited alongside, same era.
Towards arabic sentence simplification via classification and generative approaches
Nouran Khallaf and Serge Sharoff. 2022 · 2022
Later among the works it cites.
Improving iterative text revision by learning where to edit from other revision tasks
Zae Myung Kim, Wanyu Du, Vipul Raheja, Dhruv Kumar, and Dongyeop Kang. 2022 · 2022
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Coauthor: Designing a human-AI collaborative writing dataset for exploring language model capabilities
Mina Lee, Percy Liang, and Qian Yang. 2022 · 2022
Later among the works it cites.
Diffusion-lm improves controllable text generation
Xiang Li, John Thickstun, Ishaan Gulrajani, Percy S Liang, and Tatsunori B Hashimoto. 2022 · 2022
Later among the works it cites.
EdiT5: Semi-autoregressive text editing with t5 warm-start
Jonathan Mallinson, Jakub Adamek, Eric Malmi, and Aliaksei Severyn. 2022 · 2022
Later among the works it cites.
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Automatically neutralizing subjective bias in text
Reid Pryzant, Richard Diehl Martinez, Nathan Dass, Sadao Kurohashi, Dan Jurafsky, and Diyi Yang. 2020 · 2020
Cited alongside, same era.
SimplifyUR: Unsupervised lexical text simplification for Urdu
Namoos Hayat Qasmi, Haris Bin Zia, Awais Athar, and Agha Ali Raza. 2020 · 2020
Cited alongside, same era.
Benchmarking data-driven automatic text simplification for German
Andreas Säuberli, Sarah Ebling, and Martin Volk. 2020 · 2020
Cited alongside, same era.
BERTScore: Evaluating text generation with BERT
Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger, and Yoav Artzi. 2020 · 2020
Cited alongside, same era.
The (un)suitability of automatic evaluation metrics for text simplification
Fernando Alva-Manchego, Carolina Scarton, and Lucia Specia. 2021 · 2021
Cited alongside, same era.
Wordcraft: a human-ai collaborative editor for story writing
Andy Coenen, Luke Davis, Daphne Ippolito, Emily Reif, and Ann Yuan. 2021 · 2021
Cited alongside, same era.
MUSS: Multilingual unsupervised sentence simplification by mining paraphrases
Louis Martin, Angela Fan, Éric de la Clergerie, Antoine Bordes, and Benoît Sagot. 2022 · 2022
Later among the works it cites.
Neural readability pairwise ranking for sentences in Italian administrative language
Martina Miliani, Serena Auriemma, Fernando Alva-Manchego, and Alessandro Lenci. 2022 · 2022
Later among the works it cites.
Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe. 2022 · 2022
Later among the works it cites.
Learning to model editing processes
Machel Reid and Graham Neubig. 2022 · 2022
Later among the works it cites.
Findings of the TSAR-2022 shared task on multilingual lexical simplification
Horacio Saggion, Sanja Štajner, Daniel Ferrés, Kim Cheng Sheang, Matthew Shardlow, Kai North, and Marcos Zampieri. 2022 · 2022
Later among the works it cites.
Peer: A collaborative language model
Timo Schick, Jane Dwivedi-Yu, Zhengbao Jiang, Fabio Petroni, Patrick Lewis, Gautier Izacard, Qingfei You, Christoforos Nalmpantis, Edouard Grave, and Sebastian Riedel. 2022 · 2022
Later among the works it cites.
Seqdiffuseq: Text diffusion with encoder-decoder transformers
Hongyi Yuan, Zheng Yuan, Chuanqi Tan, Fei Huang, and Songfang Huang. 2022 · 2022
Later among the works it cites.
Paper plain: Making medical research papers approachable to healthcare consumers with natural language processing
Tal August, Lucy Lu Wang, Jonathan Bragg, Marti A. Hearst, Andrew Head, and Kyle Lo. 2023 · 2023
Closest in time.
Document-level planning for text simplification
Liam Cripwell, Joël Legrand, and Claire Gardent. 2023 · 2023
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RARR: Researching and revising what language models say, using language models
Luyu Gao, Zhuyun Dai, Panupong Pasupat, Anthony Chen, Arun Tejasvi Chaganty, Yicheng Fan, Vincent Zhao, Ni Lao, Hongrae Lee, Da-Cheng Juan, and Kelvin Guu. 2023 · 2023
Closest in time.
Diffuseq: Sequence to sequence text generation with diffusion models
Shansan Gong, Mukai Li, Jiangtao Feng, Zhiyong Wu, and Lingpeng Kong. 2023 · 2023
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Thresh: A unified, customizable and deployable platform for fine-grained text evaluation
David Heineman, Yao Dou, and Wei Xu. 2023b · 2023
Closest in time.
Text-Blueprint: An interactive platform for plan-based conditional generation
Fantine Huot, Joshua Maynez, Shashi Narayan, Reinald Kim Amplayo, Kuzman Ganchev, Annie Priyadarshini Louis, Anders Sandholm, Dipanjan Das, and Mirella Lapata. 2023 · 2023
Closest in time.
Co-writing with opinionated language models affects users’ views
Maurice Jakesch, Advait Bhat, Daniel Buschek, Lior Zalmanson, and Mor Naaman. 2023 · 2023
Closest in time.
SWiPE: A dataset for document-level simplification of Wikipedia pages
Philippe Laban, Jesse Vig, Wojciech Kryscinski, Shafiq Joty, Caiming Xiong, and Chien-Sheng Wu. 2023 · 2023
Closest in time.
Gpteval: Nlg evaluation using gpt-4 with better human alignment
Yang Liu, Dan Iter, Yichong Xu, Shuohang Wang, Ruochen Xu, and Chenguang Zhu. 2023 · 2023
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Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al. 2023 · 2023
Closest in time.
LENS: A learnable evaluation metric for text simplification
Mounica Maddela, Yao Dou, David Heineman, and Wei Xu. 2023 · 2023
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OpenAI. 2023 · 2023
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Revisiting non-English text simplification: A unified multilingual benchmark
Michael Ryan, Tarek Naous, and Wei Xu. 2023 · 2023
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PEER: A collaborative language model
Timo Schick, Jane A. Yu, Zhengbao Jiang, Fabio Petroni, Patrick Lewis, Gautier Izacard, Qingfei You, Christoforos Nalmpantis, Edouard Grave, and Sebastian Riedel. 2023 · 2023
Closest in time.
RewriteLM: An instruction-tuned large language model for text rewriting
Lei Shu, Liangchen Luo, Jayakumar Hoskere, Yun Zhu, Canoee Liu, Simon Tong, Jindong Chen, and Lei Meng. 2023 · 2023
Closest in time.
Patient-friendly clinical notes: Towards a new text simplification dataset
Jan Trienes, Jörg Schlötterer, Hans-Ulrich Schildhaus, and Christin Seifert. 2023 · 2023
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